Granular Neural Networks With Evolutionary Interval Learning |
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Authors: | Yan-Qing Zhang Jin B Yuchun Tang |
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Affiliation: | Georgia State Univ., Atlanta; |
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Abstract: | To deal with different membership functions of the same linguistic term, a new interval reasoning method using new granular sets is proposed based on Yin Yang methodology. To make interval-valued granular reasoning efficiently and optimize interval membership functions based on training data effectively, a granular neural network (GNN) with a new high-speed evolutionary interval learning is designed. Simulation results in nonlinear function approximation and bioinformatics have shown that the GNN with the evolutionary interval learning is able to extract interval-valued granular rules effectively and efficiently from training data by using the new evolutionary interval learning algorithm. |
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